Online risk estimation of critical and non-critical interactions between right-turning motorists and crossing cyclists by a decision tree

•Development of a real-time and infrastructure-based early warning system for both right-turning vehicles and crossing cyclists that allows estimating risks.•Application of a data-driven and machine learning approach encoding human risk perception in a decision tree.•Identification of the most supporting features for the prediction of conflicts: Gap time (i.e. predicted post encroachment time), velocity, and distance from conflict point.•Evaluation of the warning system in test drives showed reliable results.